Forest Fire Data Analysis Using Conventional Machine Learning Algorithms

Cucu Ika Agustyaningrum, Haryani Haryani, Taufik Baidawi, Wahyudin Wahyudin, Siti Marlina, Artika Surniandari, Sucitra Sahara

2023

Abstract

A forest fire is a situation in which a forest is consumed by fire, damaging the forest’s products and causing harm to the environment and the economy. Finding out how frequently forest fires occur is the aim of forest fire prediction. The process of analyzing the data is therefore carried out using traditional machine learning techniques utilizing the Random Forest, Decision Tree, Logistic Regression, Nave Bayes, and Multilayer Per-ceptron methods. Knowing the accuracy and F1 score values allows for a comparison of this method using the Python programming language. The test results showed that the multilayer peceptron approach outperformed the Random Forest, Decision Tree, Logistic Regression, and Nave Bayes methods, with accuracy values of 86.70% and 87.93%, respectively, with a hidden layer size of 32.32. When compared to the other approaches investigated, the value of the multilayer perceptron method is quite prominent. This research can help determine the probability of forest fires.

Paper Citation

in Harvard Style

Ika Agustyaningrum C., Haryani H., Baidawi T., Wahyudin W., Marlina S., Surniandari A. and Sahara S. (2023). Forest Fire Data Analysis Using Conventional Machine Learning Algorithms. In Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD; ISBN 978-989-758-678-1, SciTePress, pages 32-37. DOI: 10.5220/0012441300003848

in Bibtex Style

@conference{icaisd23,
author={Cucu Ika Agustyaningrum and Haryani Haryani and Taufik Baidawi and Wahyudin Wahyudin and Siti Marlina and Artika Surniandari and Sucitra Sahara},
title={Forest Fire Data Analysis Using Conventional Machine Learning Algorithms},
booktitle={Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD},
year={2023},
pages={32-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012441300003848},
isbn={978-989-758-678-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD
TI - Forest Fire Data Analysis Using Conventional Machine Learning Algorithms
SN - 978-989-758-678-1
AU - Ika Agustyaningrum C.
AU - Haryani H.
AU - Baidawi T.
AU - Wahyudin W.
AU - Marlina S.
AU - Surniandari A.
AU - Sahara S.
PY - 2023
SP - 32
EP - 37
DO - 10.5220/0012441300003848
PB - SciTePress